Recent Advances in Imaging of Dopaminergic Neurons for Evaluation of Neuropsychiatric Disorders

Dopamine is the most intensely studied monoaminergic neurotransmitter. Dopaminergic neurotransmission plays an important role in regulating several aspects of basic brain function, including motor, behavior, motivation, and working memory. To date, there are numerous positron emission tomography (PET) and single photon emission computed tomography (SPECT) radiotracers available for targeting different steps in the process of dopaminergic neurotransmission, which permits us to quantify dopaminergic activity in the living human brain. Degeneration of the nigrostriatal dopamine system causes Parkinson's disease (PD) and related Parkinsonism. Dopamine is the neurotransmitter that has been classically associated with the reinforcing effects of drug abuse. Abnormalities within the dopamine system in the brain are involved in the pathophysiology of attention deficit hyperactivity disorder (ADHD). Dopamine receptors play an important role in schizophrenia and the effect of neuroleptics is through blockage of dopamine D2 receptors. This review will concentrate on the radiotracers that have been developed for imaging dopaminergic neurons, describe the clinical aspects in the assessment of neuropsychiatric disorders, and suggest future directions in the diagnosis and management of such disorders

Design and Analysis of the Key Management Mechanism in Evolved Multimedia Broadcast/Multicast Service

3GPP introduced the key management mechanism (KMM) in evolved multimedia broadcast/multicast service (eMBMS) to provide forward security and backward security for multicast contents. In this paper, we point out that KMM may lead to frequent rekeying and re-authentication issues due to eMBMS's characteristics: 1) massive group members; 2) dynamic group topology; and 3) unexpected wireless disconnections. Such issues expose extra load for both user equipment (UE) terminals and mobile operators. It seems prolonging the rekeying interval is an intuitive solution to minimizing the impact of the issues. However, a long rekeying interval is not considered the best operational solution due to revenue loss of content providers. This paper quantifies the tradeoff between the load of the UEs and the operators as well as the revenue loss of the content providers. Moreover, we emphasize how essential this rekeying interval has impacts on the problems. Using our proposed tradeoff model, the operators can specify a suitable rekeying interval to best balance the interest between the above three parties. The tradeoff model is validated by extensive simulations and is demonstrated to be an effective approach for the tradeoff analysis and optimization on eMBMS

The Role of Molecular Imaging in the Diagnosis and Management of Neuropsychiatric Disorders

Neuropsychiatric disorders are becoming a major socioeconomic burden to modern society. In recent years, a dramatic expansion of tools has facilitated the study of the molecular basis of neuropsychiatric disorders. Molecular imaging has enabled the noninvasive characterization and quantification of biological processes at the cellular, tissue, and organism levels in intact living subjects. This technology has revolutionized the practice of medicine and has become critical to quality health care. New advances in research on molecular imaging hold promise for personalized medicine in neuropsychiatric disorders, with adjusted therapeutic doses, predictable responses, reduced adverse drug reactions, early diagnosis, and personal health planning. In this paper, we discuss the development of radiotracers for imaging dopaminergic, serotonergic, and noradrenergic systems and β-amyloid plaques. We will underline the role of molecular imaging technologies in various neuropsychiatric disorders, describe their unique strengths and limitations, and suggest future directions in the diagnosis and management of neuropsychiatric disorders

Lightly Weighted Automatic Audio Parameter Extraction for the Quality Assessment of Consensus Auditory-Perceptual Evaluation of Voice

The Consensus Auditory-Perceptual Evaluation of Voice is a widely employed tool in clinical voice quality assessment that is significant for streaming communication among clinical professionals and benchmarking for the determination of further treatment. Currently, because the assessment relies on experienced clinicians, it tends to be inconsistent, and thus, difficult to standardize. To address this problem, we propose to leverage lightly weighted automatic audio parameter extraction, to increase the clinical relevance, reduce the complexity, and enhance the interpretability of voice quality assessment. The proposed method utilizes age, sex, and five audio parameters: jitter, absolute jitter, shimmer, harmonic-to-noise ratio (HNR), and zero crossing. A classical machine learning approach is employed. The result reveals that our approach performs similar to state-of-the-art (SOTA) methods, and outperforms the latent representation obtained by using popular audio pre-trained models. This approach provide insights into the feasibility of different feature extraction approaches for voice evaluation. Audio parameters such as jitter and the HNR are proven to be suitable for characterizing voice quality attributes, such as roughness and strain. Conversely, pre-trained models exhibit limitations in effectively addressing noise-related scorings. This study contributes toward more comprehensive and precise voice quality evaluations, achieved by a comprehensively exploring diverse assessment methodologies.Comment: Published in IEEE 42th International Conference on Consumer Electronics (ICCE 2024

Effects of unilateral eccentric versus concentric training of nonimmobilized arm during immobilization

Introduction The present study tested the hypothesis that eccentric training (ET) of nonimmobilized arm would attenuate negative effects of immobilization and provide greater protective effects against muscle damage induced by eccentric exercise after immobilization, when compared with concentric training (CT). Methods Sedentary young men were placed to ET, CT, or control group (n = 12 per group), and their nondominant arms were immobilized for 3 wk. During the immobilization period, the ET and CT groups performed five sets of six dumbbell curl eccentric-only and concentric-only contractions, respectively, at 20%-80% of maximal voluntary isometric contraction (MVCiso) strength over six sessions. MVCiso torque, root-mean square (RMS) of electromyographic activity during MVCiso, and bicep brachii muscle cross-sectional area (CSA) were measured before and after immobilization for both arms. All participants performed 30 eccentric contractions of the elbow flexors (30EC) by the immobilized arm after the cast was removed. Several indirect muscle damage markers were measured before, immediately after, and for 5 d after 30EC. Results ET increased MVCiso (17% ± 7%), RMS (24% ± 8%), and CSA (9% ± 2%) greater (P \u3c 0.05) than CT (6% ± 4%, 9% ± 4%, 3% ± 2%) for the trained arm. The control group showed decreases in MVCiso (-17% ± 2%), RMS (-26% ± 6%), and CSA (-12% ± 3%) for the immobilized arm, but these changes were attenuated greater (P \u3c 0.05) by ET (3% ± 3%, -0.1% ± 2%, 0.1% ± 0.3%) than CT (-4% ± 2%, -4% ± 2%, -1.3% ± 0.4%). Changes in all muscle damage markers after 30EC were smaller (P \u3c 0.05) for the ET and CT than the control group, and ET than the CT group (e.g., peak plasma creatine kinase activity: ET, 860 ± 688 IU L-1; CT, 2390 ± 1104 IU L-1; control, 7819 ± 4011 IU L-1). Conclusions These results showed that ET of the nonimmobilized arm was effective for eliminating the negative effects of immobilization and attenuating eccentric exercise-induced muscle damage after immobilization

Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

<p>Abstract</p> <p>Background</p> <p>The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.</p> <p>Results and discussion</p> <p>To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis.</p> <p>Conclusion</p> <p>Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.</p

Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection

The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise.Comment: published in IEEE Journal of Translational Engineering in Health & Medicin